mini wrangle

class: center, middle, inverse, title-slide

# mini wrangle
## made with flipbookr and xaringan
### Gina Reynolds, January 2020

---

```r
library(gapminder)
library(tidyverse)
```

```
## ── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──
```

```
## ✓ ggplot2 3.3.0.9000     ✓ purrr   0.3.3     
## ✓ tibble  2.1.3          ✓ dplyr   0.8.4     
## ✓ tidyr   1.0.2          ✓ stringr 1.4.0     
## ✓ readr   1.3.1          ✓ forcats 0.4.0
```

```
## ── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
```

```r
knitr::opts_chunk$set(cache = F, comment = "")
```

---

class: split-40
count: false
.column[.content[

```r
*gapminder
```
]]
.column[.content[

```
# A tibble: 1,704 x 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# … with 1,694 more rows
```
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
* distinct(country, continent)
```
]]
.column[.content[

```
# A tibble: 142 x 2
   country     continent
   <fct>       <fct>    
 1 Afghanistan Asia     
 2 Albania     Europe   
 3 Algeria     Africa   
 4 Angola      Africa   
 5 Argentina   Americas 
 6 Australia   Oceania  
 7 Austria     Europe   
 8 Bahrain     Asia     
 9 Bangladesh  Asia     
10 Belgium     Europe   
# … with 132 more rows
```
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  distinct(country, continent) %>%  
* # tally and count are the same
* count(continent)
```
]]
.column[.content[

```
# A tibble: 5 x 2
  continent     n
  <fct>     <int>
1 Africa       52
2 Americas     25
3 Asia         33
4 Europe       30
5 Oceania       2
```
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  distinct(country, continent) %>%  
  # tally and count are the same
  count(continent) %>%  
* rename(count = n)
```
]]
.column[.content[

```
# A tibble: 5 x 2
  continent count
  <fct>     <int>
1 Africa       52
2 Americas     25
3 Asia         33
4 Europe       30
5 Oceania       2
```
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  distinct(country, continent) %>%  
  # tally and count are the same
  count(continent) %>%  
  rename(count = n) ->  
*count_in_continents
```
]]
.column[.content[

]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  distinct(country, continent) %>%  
  # tally and count are the same
  count(continent) %>%  
  rename(count = n) ->  
count_in_continents  
* # another pipeline
*gapminder
```
]]
.column[.content[

```
# A tibble: 1 x 2
  mean_life_exp median_life_exp
          <dbl>           <dbl>
1          59.5            60.7
```
]]
---
class: split-40
count: false
.column[.content[

]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  distinct(country, continent) %>%  
  # tally and count are the same
  count(continent) %>%  
  rename(count = n) ->  
count_in_continents  
  # another pipeline
gapminder %>%  
  summarize(mean_life_exp = mean(lifeExp),  
            median_life_exp = median(lifeExp)) ->  
overall_summaries  
* # another
*gapminder
```
]]
.column[.content[

```
# A tibble: 1,704 x 6
# Groups:   continent [5]
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# … with 1,694 more rows
```
]]
---
class: split-40
count: false
.column[.content[

```
# A tibble: 5 x 3
  continent mean_life_exp median_life_exp
  <fct>             <dbl>           <dbl>
1 Africa             48.9            47.8
2 Americas           64.7            67.0
3 Asia               60.1            61.8
4 Europe             71.9            72.2
5 Oceania            74.3            73.7
```
]]
---
class: split-40
count: false
.column[.content[

]]